Global policies on assistive robots for care of the elderly: A scoping review
Bibliographic record
Abstract
The elderly are the fastest growing portion of the world population. The majority of elderly want to remain independent as long as possible, with responsibility for their care often falling to family or caregivers. Assistive robots could help maintain independence in the elderly while relieving the burden of care on families and healthcare professionals. This scoping review seeks to examine the type and scope of global policies on the use of robotic technology for care of the elderly in international jurisdictions and to assess how they align with current Canadian policies. This review also seeks to determine current perceptions on the use of robotics in care of the elderly and potential barriers to their use that policy makers could encounter. A comprehensive literature search was conducted for articles related to robotic care of the elderly, perceptions of robotic care of the elderly and related policies, using a global lens. A three-step strategy was used to review and identify articles. The search identified 10 primary and secondary studies and 13 grey literature sources. Studies reported that response to robotic care for the elderly had both positive and negative aspects, and that concerns around privacy and cost were prevalent. Japan and the EU had the most comprehensive policy strategies and proposals. Robotic policy in healthcare is relatively new but will become increasingly important in the coming years. Canada needs to strengthen and anticipate its national policy strategy to ensure it can stay aligned with the fast pace of technological change. Further robust research should continue to explore potential for, and concerns over robotic care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".